White Images Research Articles

Unsupervised adversarial deep domain adaptation method for potato defects classification

In the last few years, deep learning methods have been proposed to automate the quality control of various agricultural products. Despite the excellent results obtained, one of their main drawbacks is the need for a large annotated dataset to obtain satisfactory performance. Building this dataset is time-consuming and tedious. Moreover, in some real-world applications, vision systems can be modified over time. Such changes may generate a drop in network performance trained with the initial database (source images). To avoid the creation of a new labeled database every time a change in data distribution occurs (target images), several domain adaptation methods have been proposed. In this article, we introduce an unsupervised deep domain adaptation method based on adversarial training. A large dataset is used, including six classes of potatoes: healthy, damaged, greening, black dot, common scab, and black scurf. Two different scenarios of domain adaptation problem are considered. Firstly, a simply modification of the image acquisition system is simulated by artificially increasing the brightness of some white potatoes images (target images). Secondly, a significantly different dataset including red potatoes is introduced. In this setting, white potatoes are used as source images and red tubers as target images. We propose to train a target classifier using a pseudo-label loss, due to the unavailability of target annotations. Experimental results show that a domain adaptation method is mandatory, going from an average F1-score of 0.46 without adaptation, to 0.84 by applying our method. Finally, a comparative analysis is achieved showing that adversarial-based unsupervised domain adaptation methods outperform discrepancy-based approaches.

Dynamic substitution based encryption algorithm for highly correlated data

In this paper, single and multiple S-box encryption issues are discussed and a new algorithm is then proposed to solve those issues. As the single S-box encryption completely fails to encrypt the plain image because a single S-box replaces the pixels of the same region with a unique symbol. To break this type of correlation in the image, chaos has been extensively used. The multiple S-boxes techniques are becoming more popular over the last decade to overcome the drawbacks of single S-box encryption. However, replacing single S-box with multiple S-boxes is somehow good for the encryption of those images which contain more number gray levels i.e 256 gray levels, but it cannot properly conceal the pixels of an image having less number of gray levels i.e binary or plain white images which contain only one gray level. To solve these issues, a dynamic substitution based encryption algorithm (DSA) is proposed, which is specially designed for the highly correlated images. Security analysis and evaluation show that the proposed algorithm can accomplish to conceal the highly correlated data.

Implementation of Genetic Algorithms and Momentum Backpropagation in Classification of Subtype Cells Acute Myeloid Leukimia

Acute Myeloid Leukimia (AML) is a type of cancer which attacks white blood cells from myeloid. AML subtypes M1, M2, and M3 are affected by the same type of cells called myeloblasts, so it needs more detailed analysis to classify.Momentum Backpropagation is used to classified. In its application, optimal selection of architecture, learning rate, and momentum is still done by random trial. This is one of the disadvantage of Momentum Backpropagation. This study uses a genetic algorithm (GA) as an optimization method to get the best architecture, learning rate, and momentum of artificial neural network. Genetic algorithms are one of the optimization techniques that emulate the process of biological evolution.The dataset used in this study is numerical feature data resulting from the segmentation of white blood cell images taken from previous studies which has been done by Nurcahya Pradana Taufik Prakisya. Based on these data, an evaluation of the Momentum Backpropagation process was conducted the selection parameter in a random trial with the genetic algorithm. Furthermore, the comparison of accuracy values was carried out as an alternative to the ANN learning method that was able to provide more accurate values with the data used in this study.The results showed that training and testing with genetic algorithm optimization of ANN parameters resulted in an average memorization accuracy of 83.38% and validation accuracy of 94.3%. Whereas in other ways, training and testing with momentum backpropagation random trial resulted in an average memorization accuracy of 76.09% and validation accuracy of 88.22%.

Bwimage: A package to describe image patterns in natural structures.

Currently R is the most popular software for data analyses among biologists. Here, we present bwimage, a package designed to describe patterns from black and white images. The package can be used for a wide range of applications. We implemented functions previously described in the literature to calculate parameters designed originally, but not exclusively, for vegetation structures. Additionally, we propose a new parameter: the aggregation index. We demonstrate applications for field work, providing examples that range from calculation of canopy openness, description of patterns in vertical vegetation structure, to patterns in bird nest structure. We provide advice and illustrated examples of how to produce high quality images for analyses.

Quantitative susceptibility mapping depicts severe myelin deficit and iron deposition in a transgenic model of multiple system atrophy

Despite internationally established diagnostic criteria, multiple system atrophy (MSA) is frequently misdiagnosed, particularly at disease onset. While neuropathological changes such as demyelination and iron deposition are typically detected in MSA, these structural hallmarks were so far only demonstrated post-mortem. Here, we examine whether myelin deficit observed in a transgenic murine model of MSA can be visualized and quantified in vivo using specific magnetic resonance imaging (MRI) approaches. Reduced myelin content was measured histologically in prototypical white matter as well as mixed grey-white matter regions i.e. corpus callosum, anterior commissure, and striatum of transgenic mice overexpressing human α-synuclein under the control of the myelin basic protein promotor (MBP29-hα-syn mice). Correspondingly, in vivo quantitative susceptibility mapping (QSM) showed a strongly reduced susceptibility contrast in white matter regions and T2-weighted MR imaging revealed a significantly reduced grey-white matter contrast in MBP29-hα-syn mice. In addition, morphological analysis suggested a pronounced, white matter-specific deposition of iron in MBP29-hα-syn mice. Importantly, in vivo MRI results were matched by comprehensive structural characterization of myelin, iron, and axonal directionality. Taken together, our results provide strong evidence that QSM is a very sensitive tool measuring changes in myelin density in conjunction with iron deposition in MBP29-hα-syn mice. This multimodal neuroimaging approach may pave the way towards a novel non-invasive technique to detect crucial neuropathological changes specifically associated with MSA.

Efficient symmetric image encryption by using a novel 2D chaotic system

The authors know that a common and effective way to protect digital images security are to encrypt these images into white noise image. In this study, the authors have designed a new two-dimensional (2D) chaotic system which is derived from two existing one-dimensional (1D) chaotic maps. The simulation results show that the new 2D chaotic system is able to produce many 2D chaotic maps by selecting different 1D chaotic maps, and which have the wider chaotic ranges and more complex chaotic behaviours compared with the existing 1D chaotic maps. In order to investigate its applications, using the proposed 2D chaotic maps, the authors design a novel image encryption algorithm. First of all, the original image is scrambled by using the chaotic sequences which are generated by new 2D chaotic maps, Arnold transform and Hilbert curve. Then the scrambled image is confused and diffused by chaotic sequences. Finally, the performance of the proposed encryption algorithm is simulated and the experimental results show that the validity and reliability of the proposed algorithm is validated.

The Art of Solidarity: Visual and Performative Politics in Cold War Latin America, edited by Jessica Stites Mor and María del Carmen Suescun Pozas (Austin: University of Texas Pr

The Art of Solidarity: Visual and Performative Politics in Cold War Latin America, edited by Jessica Stites Mor and Maria del Carmen Suescun Pozas (Austin: University of Texas Press, 2018), 310 pages, Black and white images What does it mean when an artist sends an artwork in solidarity with Salvador Allende’s government in Chile, joining other artists who may or may not know each other, separated by national borders? What is the significance of boycotting the Sao Paulo biennial in solidari...

Nonmydriatic widefield retinal imaging with an automatic white LED confocal imaging system compared with dilated ophthalmoscopy in screening for diabetic retinopathy.

To compare nonmydriatic montage widefield images with dilated fundus ophthalmoscopy for determining diabetic retinopathy (DR) severity. In this prospective, observational, cross-sectional study, patients with a previous diagnosis of diabetes and without history of diabetes-associated ocular disease were screened for DR. Montage widefield imaging was obtained with a system that combines confocal technology with white-light emitting diode (LED) illumination (DRSplus, Centervue, Padua, Italy). Dilated fundus examination was performed by a retina specialist. Thirty-seven eyes (20 patients, 8 females) were finally included in the analysis. Mean age of the patients enrolled was 58.0 ± 11.6years [range 31-80years]. The level of DR identified on montage widefield images agreed exactly with indirect ophthalmoscopy in 97.3% (36) of eyes and was within 1 step in 100% (37) of eyes. Cohen's kappa coefficient (κ) was 0.96, this suggesting an almost perfect agreement between the two modalities in DR screening. Nonmydriatic montage widefield imaging acquisition time was significantly shorter than that of dilated clinical examination (p = 0.010). Nonmydriatic montage widefield images were compared favorably with dilated fundus examination in defining DR severity; however, they are acquired more rapidly.

Mal2d: 2d Based Deep Learning Model for Malware Detection Using Black and White Binary Image

Mal2d: 2d Based Deep Learning Model for Malware Detection Using Black and White Binary Image

DeFord Bailey in Country Music's Multiracial Canon

DeFord Bailey (1899–1982), an African American harmonica virtuoso, performed regularly on the Grand Ole Opry radio program from 1926 to 1941 and afterward fell into obscurity. Decades later, however, he was inducted into the Country Music Hall of Fame (2005), overseen by the Country Music Association (CMA), amid calls to diversify a predominantly white country music canon. Motivated by racially progressive ideals and seeking to rehabilitate the genre's image, many fans and industry advocates misrepresented Bailey's achievements in the surrounding conversations, or they relied upon essentializing notions of black music in their advocacy on his behalf. Resistance to his candidacy for the Hall was cited as evidence of the industry's institutionalized racism. While his eventual induction allowed the CMA some room in which to refute that charge and promote a multiracial narrative for the genre's history, consistent with its long-standing desire to cultivate middle-class respectability, that same multiracial narrative obscured Bailey's role in the production of a distinctly white image for country music in the 1920s and 1930s. Highlighting this discrepancy, this article compares the historical and contemporary reception of Bailey's music and legacy, drawing upon newspaper accounts, Opry promotional materials, archival interviews, and commercial recordings. Opry broadcasts played host to blues, blackface, and other racially coded repertoires; Bailey's blues-based style did not distinguish him from his white Opry peers. Opry marketing worked assiduously to present a singular white image for the show and its repertoire, marginalizing or obscuring Bailey's racial identity in its programming and publicity. In this manner, Bailey's career has paradoxically been made to serve narratives asserting both the whiteness and the multiracialism of country music.

Automatic Image Colorization using Deep Learning

Image colorization is a fascinating topic and has become an area of research in the recent years. In this project, we are going to colorize black and white images with the help of Deep Learning techniques. Some previous approaches required human involvement or resulted in the development of desaturated images. We are building a Deep Convolutional Neural Network (CNN) which will be trained on over a million images. The output generated by the model is fully dependent on the images it has been trained from and requires no human help. The images are taken from different sources like ResNet, Reddit, etc. The model will include many hidden layers to make the output more accurate. This will be a fully automatic model and will produce images with accurate colors and contrast. Finally, the goal of this project is to produce realistic and color accurate images that can easily fool the viewer. The viewer wouldn’t be able to differentiate between the photo which the model produced and the real photo. Our project has wide practical applications like historical image/video restoration, image enhancement for better interpretability, frame by frame colorization of black and white documentaries, etc.

A Framework for Grading of White Chali Type Arecanuts with Machine Learning Algorithms

Grading of arecanuts before marketing fetches better prize. There are two universally accepted grading systems for arecanuts namely grading at the producers’ level and grading at the wholesale traders’ level. The major work in this paper is devoted to generation of a standard image database for the White Chali Type arecanuts and constructing a frame work for their grading by exploring the features of White Chali Type arecanut images for the first time. Further, two separate datasets are developed for the above grading systems by employing image feature extraction methods with 3500 and 4132 instances respectively. The arecanuts are graded using popular machine learning algorithms and the results are validated using ten fold cross validation. Multinomial logistic regression as classifier outperformed with classification accuracies of 98.8% and 92.69% for the producers’ level and the whole sale traders’ level grading systems respectively. Also the performances of various machine learning algorithms for the above two datasets are evaluated using weighted average values of True Positive rate, False Positive rate, precision, recall, F-measure and Cohen’s Kappa coefficient.

PTSD, but not history of mTBI, is associated with altered myelin in combat-exposed Iraq and Afghanistan Veterans

Objective To investigate the biological, cognitive, and psychological presentations of combat-exposed Veterans with a history of mild traumatic brain injury (mTBI) and/or posttraumatic stress disorder (PTSD) using a novel white matter imaging technique and comprehensive neuropsychological assessment. Method 74 Iraq and Afghanistan Veterans (mean age 33.89, 90.5% male) with history of mTBI (average 7.25 years since injury), PTSD, both, or neither underwent magnetic resonance imaging (MRI) exams including acquisition of a novel imaging technique, multicomponent-driven equilibrium single-pulse observation of T1/T2 (mcDESPOT) to quantify myelin water fraction (MWF), a surrogate measure of myelin content. Participants also underwent comprehensive neuropsychological assessment and three cognitive composite scores (memory, working memory/processing speed, and executive functioning) were created. Results There were no significant group differences on the neuropsychological composite scores. ANCOVAs revealed a main effect of PTSD across all a priori regions of interest (ROI) in which PTSD was associated with higher MWF. There was no main effect of mTBI history or TBI by PTSD interaction on any ROI. Significant positive associations were observed between myelin and PTSD symptoms, but no significant associations were found between myelin and neurobehavioral symptoms. No significant associations were found between myelin in the a priori ROIs and the cognitive composite scores. Conclusion This study did not find neuropsychological or MWF differences in combat Veterans with a remote history of mTBI but did find myelin alterations related to PTSD. Psychological trauma should be a primary target for intervention in Veterans with comorbid PTSD and mTBI reporting subjective complaints, given its salience.

FV9 Structural grey and white matter imaging patterns differ between bulbar and limb-onset Amyotrophic Lateral Sclerosis (ALS): Cross-sectional subgroup analyses with application of the D50 disease progression model

FV9 Structural grey and white matter imaging patterns differ between bulbar and limb-onset Amyotrophic Lateral Sclerosis (ALS): Cross-sectional subgroup analyses with application of the D50 disease progression model

Revealing Fine Structures of the Retinal Receptive Field by Deep-Learning Networks.

Deep convolutional neural networks (CNNs) have demonstrated impressive performance on many visual tasks. Recently, they became useful models for the visual system in neuroscience. However, it is still not clear what is learned by CNNs in terms of neuronal circuits. When a deep CNN with many layers is used for the visual system, it is not easy to compare the structure components of CNNs with possible neuroscience underpinnings due to highly complex circuits from the retina to the higher visual cortex. Here, we address this issue by focusing on single retinal ganglion cells with biophysical models and recording data from animals. By training CNNs with white noise images to predict neuronal responses, we found that fine structures of the retinal receptive field can be revealed. Specifically, convolutional filters learned are resembling biological components of the retinal circuit. This suggests that a CNN learning from one single retinal cell reveals a minimal neural network carried out in this cell. Furthermore, when CNNs learned from different cells are transferred between cells, there is a diversity of transfer learning performance, which indicates that CNNs are cell specific. Moreover, when CNNs are transferred between different types of input images, here white noise versus natural images, transfer learning shows a good performance, which implies that CNNs indeed capture the full computational ability of a single retinal cell for different inputs. Taken together, these results suggest that CNNs could be used to reveal structure components of neuronal circuits, and provide a powerful model for neural system identification.

Visualizing civic values: Representations of idealized citizenship behaviours in images found in Nepalese social studies textbooks

Citizenship and political education has increasingly been identified as an important component in educating young people for democratic citizenship. The National Curriculum Framework for School Education in Nepal 2007, the Nepalese social studies school curriculum, and textbooks explicitly underline the promotion of a democratic system and culture along with the development of civic and citizenship skills among Nepalese citizens. This article reports on a qualitative analysis of selected civic awareness images in Nepalese social studies textbooks using a ‘visual grammar’ framework. The analysis reveals that the images attempt to promote a message of national unity and respect for diversity in the country. However, the low quality black and white images and textbooks have some adverse teaching and learning implications for teachers and students and tend towards idealized representations.

DETECTION OF LEUKEMIA IN MICROSCOPIC WHITE BLOOD CELL IMAGES USING GAUSSIAN FEATURE CONVOLUTIONAL VISUAL RECOGNITION ALGORITHM

Cancer detection has always been a challenge in the diagnosis and treatment plan for hematological diseases. Manual identification of cancers from microscopic images is necessary. It depends on other factors, including the lack of microscopic images as their knowledge and precise measurement of specific usual or cancer expert classifications. Automated identification of cancer cells from microscopic section images and helps to improve the problem mentioned above, based on biological interpretation. Detection of blood cancer cells by a patient's blood mark microscopic examination using different techniques of image processing. White blood cell cancer images and their essential three-stage there are an enhancement, segmentation, and classification. The stage of image processing to achieve more quality and accuracy in detecting blood cancers. Approaching the conventional segmentation and counting of blood cells is considered as an essential step that helps to extract features to diagnose diseases like leukemia. The image analysis will allow hematologist experts to perform faster and more accurately. In this work, the image recognition problem of White Blood Cells (WBC) cancer is investigated. Different types of white blood cells are classified using a Gaussian Feature Convolutional Visual Recognition (GFCVR).The most important features or segments of these blood cells are provided as input microscopic images to the neural network. The consequences of this analysis are mainly considering the identification of blood cancer affected region with feature extraction for specific white blood cell images. In this proposed approach pre-processing the image, classifies multi-level clustering to find the affected area in blood cells. In this proposed simulation, results in time complexity, accuracy, recall, perception, false classification ratio gives better results compare with other existing methods.

Microlens Array Grid Estimation, Light Field Decoding, and Calibration

We quantitatively investigate multiple algorithms for microlens array grid estimation for microlens array-based light field cameras. Explicitly taking into account natural and mechanical vignetting effects, we propose a new method for microlens array grid estimation that outperforms the ones previously discussed in the literature. To quantify the performance of the algorithms, we propose an evaluation pipeline utilizing application-specific ray-traced white images with known microlens positions. Using a large dataset of synthesized white images, we thoroughly compare the performance of the different estimation algorithms. As an example, we apply our results to the decoding and calibration of light fields taken with a Lytro Illum camera. We observe that decoding as well as calibration benefit from a more accurate, vignetting-aware grid estimation, especially in peripheral subapertures of the light field.

In and Out of Sight: The Afterlife of Official Photography from Idi Amin's Uganda

This article examines the output of Uganda's official Photographic Section from the years of the Idi Amin regime (1971-9), an archive of 60,000 black and white images from which have recently come to light in the stores of the Uganda Broadcasting Corporation in Kampala. Drawing on recent developments in African visual studies, the article focuses in particular upon what this archive reveals about photographic circulations in and from Amin's Uganda. It finds that this new trove of negatives -when compared with press archives from around the world - is especially revealing of a growing nexus between official photography and all kinds of commercial photographies in the 1970s. This nexus played a key role in shaping both how the Amin regime was pictured at the time, and how its afterlife has continued to reflect down to the present time.

Investigation of PPLN Waveguide Uniformity via Second Harmonic Generation Spectra

Experimental data collection methods and a corresponding numerical model are presented to investigate the quality of waveguide fabrication in nonlinear optics. The method utilises white light interferometry and standard image recognition techniques to calculate a waveguide propagation constant function. This enables comparison of a numerical second harmonic spectrum in quasi-phasematched materials, such as periodically poled lithium niobate, with the waveguide’s experimental phasematching spectrum. Using the presented method, a 3rd order polynomial fit to waveguide ridge width is demonstrated to be in good agreement with experimental phasematching spectra. The presented technique provides a nondestructive route to discriminate between issues in fabrication steps in nonlinear waveguide design.